Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation

The dynamics and the stationary states for the competition between pattern reconstruction and asymmetric sequence processing are studied here in an exactly solvable feed-forward layered neural network model of binary units and patterns near saturation. Earlier work by Coolen and Sherrington on a par...

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Bibliographic Details
Published in:Physical review. E, Statistical, nonlinear, and soft matter physics Statistical, nonlinear, and soft matter physics, 2005-08, Vol.72 (2 Pt 1), p.021908-021908, Article 021908
Main Authors: Metz, F L, Theumann, W K
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
Feedback
Models, Neurological
Models, Statistical
Neural Networks (Computer)
Pattern Recognition, Automated - methods
Sequence Analysis - methods
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Summary:The dynamics and the stationary states for the competition between pattern reconstruction and asymmetric sequence processing are studied here in an exactly solvable feed-forward layered neural network model of binary units and patterns near saturation. Earlier work by Coolen and Sherrington on a parallel dynamics far from saturation is extended here to account for finite stochastic noise due to a Hebbian and a sequential learning rule. Phase diagrams are obtained with stationary states and quasiperiodic nonstationary solutions. The relevant dependence of these diagrams and of the quasiperiodic solutions on the stochastic noise and on initial inputs for the overlaps is explicitly discussed.
ISSN:1539-3755
1550-2376
DOI:10.1103/PhysRevE.72.021908